ArticleOncotarget2016
Prediction and diagnosis of renal cell carcinoma using nuclear magnetic resonance-based serum metabolomics and self-organizing maps.
Article in Oncotarget, 2016. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 38 papers, 2 of them syntheses that pooled it.
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Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
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Who cites it
38 citing papers in PubMed, 2 syntheses or guidelines pooled it.
- Revolutionizing spinal interventions: a systematic review of artificial intelligence technology applications in contemporary surgery.BMC surgery · 2024Pooled it
- Artificial intelligence in urolithiasis: a systematic review of utilization and effectiveness.World journal of urology · 2024Pooled it
- The Study on the Pathogenesis of Pediatric Lymphoma Based on the Combination of Pseudotargeted and Targeted Metabolomics.BioMed research international · 2021Trial
- Nuclear Magnetic Resonance metabolomics reveals an excretory metabolic signature of renal cell carcinoma.Scientific reports · 2016Trial
- Identification of biomarkers for renal cell carcinoma in plasma samples measured using liquid chromatography-mass spectrometry and gas chromatography-mass spectrometry.Scientific reports · 2025Article
- Disclosing the development and focus of sequencing and omics studies in kidney neoplasm research.Discover oncology · 2025Article
- Metabolomics for the Identification of Biomarkers in Kidney Diseases.Nanotheranostics · 2025Review
- Article
- Wide-Targeted Semi-Quantitative Analysis of Acidic Glycosphingolipids in Cell Lines and Urine to Develop Potential Screening Biomarkers for Renal Cell Carcinoma.International journal of molecular sciences · 2024Article
- Applications of artificial intelligence in the diagnosis and prediction of erectile dysfunction: a narrative review.International journal of impotence research · 2023Review
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- Molecular signature of renal cell carcinoma by means of a multiplatform metabolomics analysis.Biochemistry and biophysics reports · 2022Article
- Management of metastatic renal cell carcinoma following prior vascular endothelial growth factor-targeted therapy: A real-world retrospective study from Taiwan.Journal of the Chinese Medical Association : JCMA · 2022Article
- Article
- Artificial Intelligence Applications in Urology: Reporting Standards to Achieve Fluency for Urologists.The Urologic clinics of North America · 2022Review
- Precision Medicine: An Optimal Approach to Patient Care in Renal Cell Carcinoma.Frontiers in medicine · 2022Review
- 1H-NMR Based Metabolomics Technology Identifies Potential Serum Biomarkers of Colorectal Cancer Lung Metastasis in a Mouse Model.Cancer management and research · 2022Article
- Diagnostic Impact of Radiological Findings and Extracellular Vesicles: Are We Close to Radiovesicolomics?Biology · 2021Review
- Gender-Specific Metabolomics Approach to Kidney Cancer.Metabolites · 2021Article
Corrections and comments
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Authors and funding
10 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Diagnosis of renal cell carcinoma (RCC) at an early stage is challenging, but it provides the best chance for cure. We aimed to develop a predictive diagnostic method for early-stage RCC based on a biomarker cluster using nuclear magnetic resonance (NMR)-based serum metabolomics and self-organizing maps (SOMs). We trained and validated the SOM model using serum metabolome data from 104 participants, including healthy individuals and early-stage RCC patients. To assess the predictive capability of the model, we analyzed an independent cohort of 22 subjects. We then used our method to evaluate changes in the metabolic patterns of 23 RCC patients before and after nephrectomy. A biomarker cluster of 7 metabolites (alanine, creatine, choline, isoleucine, lactate, leucine, and valine) was identified for the early diagnosis of RCC. The trained SOM model using a biomarker cluster was able to classify 22 test subjects into the appropriate categories. Following nephrectomy, all RCC patients were classified as healthy, which was indicative of metabolic recovery. But using a diagnostic criterion of 0.80, only 3 of the 23 subjects could not be confidently assessed as metabolically recovered after nephrectomy. We successfully followed-up 17 RCC patients for 8 years post-nephrectomy. Eleven of these patients who diagnosed as metabolic recovery remained healthy after 8 years. Our data suggest that a SOM model using a biomarker cluster from serum metabolome can accurately predict early RCC diagnosis and can be used to evaluate postoperative metabolic recovery.
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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.